Concerns about patient safety and adverse outcomes are nowhere more acute than in obstetrics, a high risk environment in which decisions must be made quickly and coordinated by providers from several disciplines. The safety crisis is highlighted by medical malpractice judgments that have reached historic proportions when obstetrics is involved. Despite the recommendations of the Institute of Medicine and the crisis in obstetric malpractice, no validated system currently exists to improve patient safety in labor and delivery. The goal of this risk assessment planning project is to identify and evaluate the impact of system factors that contribute to variations in the process of obstetric care and expose mother and child to risk of injury. To accomplish this we will collect data from a number of sources including direct field observations, case summaries (using closed claims review, root cause analysis reports, morbidity and mortality case presentations, and outcomes birth logs), and expert opinion interviews. We will then 1) qualitatively model relationships, interactions, and critical inter-dependencies between the identified system factors and components, 2) quantitatively estimate risk, reliability, and recovery as a function of various system configurations, 3) simulate system responses using Monte Carlo techniques, 4) use simulation techniques to identify the dominant contributors to risk and forecast the effects of specific interventions, system redesigns, and risk reduction strategies, and 5) use the validated model and simulated results to plan candidate solutions and establish priorities for safety interventions and procedures. As a foundation we will use probabilistic risk assessment (PRA) methodologies that have proven useful in engineering and industrial domains. We anticipate the need to modify PRA techniques to adapt to the special properties of the medical domain. The proposed study has a number of strengths: 1) prospective field observations to capture variances in care that usually go unreported, 2) development of a coding scheme and software program with a web-based interface for primary data input backed by a relational data model for analysis of large quantities of observational data, and 3) an experienced multi-disciplinary team with expertise in obstetrics, reproductive epidemiology, human factors and informatics engineering, and probabilistic risk assessment.